Animation User Value Portrait Based on RFM Model under Big Data

Abstract

The development and application of big data technology has expanded the sources and methods for enterprises in the animation industry to obtain data, which provided them with the opportunity to obtain more user samples and solved the computing and storage problems faced by enterprises with massive data. By using statistical analysis and data mining methods for modeling, the user portrait of each user is depicted in an all-round and three-dimensional manner. Based on this background, this study proposes the topic of user portrait modeling based on the Reference Forward Model (RFM) model under big data. In this study, the animation user portrait modeling based on the RFM model under big data is firstly summarized: collect and summarize the behavior data of animation client users, prepare the data, and use entropy and Pearson correlation coefficient methods for data processing. And, then the weight of RFM is calculated through AHP (Analytic Hierarchy Process), and the behavior data collection method for animation user portrait modeling is described. Finally, based on the model tag in the constructed user portrait tag system, the RFM model under big data is used to analyze and model from multiple dimensions. In particular, in the algorithm model of animation user recognition, the weight and value of RFM are calculated to obtain the user value, and the results of the animation user portrait are summarized. Experiments proved that based on RFM Model under Big Dat can identify animation users more accurately.